Decision tree classifier
Attention: Available only with Twin Activate commercial edition.
Syntax
parameters = dtcfit(X,y)
parameters = dtcfit(X,y,options)
Inputs
- X
- Training data.
- Type: double
- Dimension: vector | matrix
- y
- Target values.
- Type: double
- Dimension: vector | matrix
- options
- Type: struct
-
- criterion
- Function to measure quality of a split. 'gini' for Gini Impurity (default) or 'entropy' for Information Gain.
- Type: char
- Dimension: string
- splitter
- Strategy used to choose the split at each node. 'best' to choose best split (default) or 'random' to choose random split.
- Type: char
- Dimension: string
- max_depth
- The maximum depth of the tree. If not assigned, nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split.
- Type: integer
- Dimension: scalar
- max_samples_split
- The minimum number of samples required to split an internal node (default: 2). If integer, consider it as the minimum number; if double, (min_samples_split * n_samples) is taken as the minimum number of samples for each split.
- Type: double | integer
- Dimension: scalar
- min_samples_leaf
- The minimum number of samples required to be at a leaf node (default: 1). If number of samples are less than min_samples_leaf at any node, tree is not built further under that node. If integer, consider it as the minimum number; if double, (min_samples_leaf * number of samples) is taken as the minimum number of samples for each node.
- Type: double | integer
- Dimension: scalar
- min_weight_fraction_leaf
- The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node (default: 0).
- Type: double
- Dimension: scalar
- max_features
- The number of features to consider when looking for the best split (default: number of features in training data). If integer, at each split, consider max_features; if double, at each split, consider floor(max_features * n_features)
- Type: double | integer
- Dimension: scalar
- random_state
- Controls the randomness of the model. At each split, features are randomly permuted. random_state is the seed used by the random number generator.
- Type: integer
- Dimension: scalar
- max_leaf_nodes
- Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined by its reduction in impurity. If not assigned, then unlimited number of leaf nodes.
- Type: integer
- Dimension: scalar
- min_impurity_decrease
- A node will be split if this split reduces the impurity >= this value (default: 0).
- Type: double
- Dimension: scalar
Outputs
- parameters
- contains all the values passed to dtcfit method as options. Additionally it has below key-value pairs.
- Type: struct
-
- scorer
- Function handle pointing to 'accuracy' function.
- Type: function handle
- classes
- The class labels (single output problem), or a matrix of class labels (multi-output problem).
- Type: double
- Dimension: vector | matrix
- max_features
- The inferred value of max_features.
- Type: integer
- Dimension: scalar
- feature_importances
- Feature importances. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as Gini Importance.
- Type: double
- Dimension: n_features
- n_samples
- Number of rows in the training data.
- Type: integer
- Dimension: scalar
- n_features
- Number of columns in the training data.
- Type: integer
- Dimension: scalar
Example
Usage of dtcfit without options
data = dlmread('iris.csv', ',', 1);
X = data(:,1:end-1);
y = data(:,end);
parameters = dtcfit(X, y);
> parameters
parameters = struct [
classes: [Matrix] 1 x 3
0 1 2
criterion: gini
feature_importances: [Matrix] 1 x 4
0.00000 0.01333 0.06406 0.92261
max_features: 4
min_impurity_decrease: 0
min_samples_leaf: 1
min_samples_split: 2
min_weight_fraction_leaf: 0
n_features: 4
n_samples: 150
splitter: best
]
Comments
It performs classification by constructing a Decision Tree. Once the tree construction is over, it can be used for prediction using dtcpredict function.
